Augmented reality diagnostic tool for data center nodes

ABSTRACT

An augmented reality (AR) diagnostic tool embodied as a software application on a portable device employs AR infrastructure to enable a user to locate a failed/malfunctioning node of a cluster and, with minimal interaction, diagnose causes and provide recommendations to repair the node. The portable device may be a computer embodied as visualization technology and configured to execute the software application. Once installed, the AR diagnostic (ARD) tool is ready for use by the user, e.g., a customer service technician, to locate and repair one or more failed cluster nodes. In response to a failure/malfunction, the cluster node sends diagnostic and configuration information (i.e., failure/malfunction information) of the failed node to an analytics service. The failure information informs the technician of the cluster failure. The technician may then activate the ARD tool and AR infrastructure to locate and repair the failed node.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/750,721, filed on Jan. 23, 2020, which is hereby incorporated byreference in its entirety for all purposes.

BACKGROUND Technical Field

The disclosure generally relates to diagnostics for nodes of a datacenter and, more specifically, to a diagnostic tool used to diagnose andrepair failures of nodes in the data center.

Background Information

A conventional enterprise configuration used to run modern applicationstypically consists of a large number of compute, storage, and networkingelements organized in a data center architecture. Such an architecturemay include, among other things, Hyper Converged Infrastructure (HCI)systems that converge these three separate elements into a singleunified system of nodes deployed as an HCI cluster so as to maintain thevarious elements from one location.

However, maintaining and administering such systems on a large datacenter scale is challenging due to sheer geographic dispersion andquantity of elements. To that end, sophisticated diagnostic tools thatenhance reliability and ease maintenance with advanced, yet easy-to-useanalysis and visualization differentiate over competing vendorofferings. An aspect of such differentiation may include dramaticallysimplifying fault detection of one or more nodes within the data center.Specifically, it may be desirable to provide a tool for use by a user,e.g., a customer service field technician, to quickly locate a failednode within a customer's data center, diagnose the failure, and obtainresolution in real time. Operation of the tool should be easy so thatthe customer service technician will use the tool for “self-diagnosis”of cluster node failure, rather than call support for initial assessmentof the root cause of the failure. Operation of the tool should furtherbe clear regarding the steps for problem discovery and subsequentguidance of the technician through these steps. The resulting effect ofthe tool is to reduce the number of support engagements, thus making theproduct easier to support as well as to rapidly assess root problemcauses.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and further advantages of the embodiments herein may be betterunderstood by referring to the following description in conjunction withthe accompanying drawings in which like reference numerals indicateidentically or functionally similar elements, of which:

FIG. 1 is a block diagram of a computing environment having a cloudprovider connected to a private customer network via a public computernetwork;

FIG. 2 is block diagram of a node of a cluster deployed in the privatecustomer network;

FIG. 3 illustrates an example workflow of an augmented realitydiagnostic (ARD) tool configured to locate, diagnose and providerecommendations to repair a failed node of the cluster;

FIG. 4 is an example rendering of information displayed as a visualoverlay on wearable visualization technology of the ARD tool; and

FIG. 5 illustrates an exemplary simplified procedure for utilizing theARD tool to locate, diagnose and provide recommendations to repair afailed node of the cluster.

OVERVIEW

The embodiments described herein are directed to an augmented reality(AR) diagnostic tool embodied as a software application on a portabledevice that employs AR infrastructure to enable a user to locate afailed or malfunctioning node of an HCI cluster in a customer datacenter and, with minimal interaction, diagnose causes and providerecommendations to functionally and quickly repair the failed ormalfunctioning node. The portable device may be a computer embodied aswearable visualization technology and configured to execute the softwareapplication that is installable from a vendor-specific web site or cloudprovider. Once installed, the software application transforms theportable device into the AR diagnostic (ARD) tool that is ready for useby the user, e.g., a customer service technician, in the data center tolocate and repair one or more failed cluster nodes. In response to asuspected failure, the cluster node sends diagnostic and configurationinformation (i.e., failure/malfunction information), including a nodeidentifier (ID), of the failed, malfunctioning, or sub-optimallyoperating node to a cloud-based analytics service of the vendor-specificcloud provider. The failure information also notifies the technician ofthe HCI cluster as to the node failure, malfunction, or suboptimaloperation using, e.g., the node ID of the failed node. The technicianmay then activate the ARD tool and AR infrastructure to locate andrepair the node. Advantageously, the ARD tool and AR infrastructureimproves reliable operation of the cluster and reduces down time, aswell as rectifies suboptimal operation, such as increasing networkthroughput by alleviating otherwise unforeseen bottlenecks based oninformation from the analytics service.

Description

FIG. 1 is a block diagram of a computing environment 100 having a cloudprovider 110 connected to a customer network 170 via a public computernetwork 140. The cloud provider 110 illustratively includes one or morecomputer nodes 120 and intermediate nodes 130 deployed as avendor-specific web site or “cloud” such as, e.g., with deployments fromNetApp Cloud Central. The cloud provider 110 may be configured toprovide services, such as a cloud-based analytics service 122 anddiagnostic service 124, that are accessible to the customer network 170over the public computer network 140, such as the Internet. Eachcomputer node 120 is illustratively embodied as a computer system havinginterconnected processor(s), main memory, storage adapter(s), andnetwork adapter(s). The network adapter connects the computer node 120to other computer nodes 120 of the cloud provider 110 over local networksegments 125 illustratively embodied as shared local area networks(LANs) or virtual LANs (VLANs).

The intermediate node 130 may be embodied as a network switch, router,or virtual private network (VPN) gateway that interconnects the LAN/VLANlocal segments 125 with remote network segments 135 illustrativelyembodied as point-to-point links, wide area networks (WANs), and/or VPNsimplemented over the public network 140, such as the Internet, andfurther connect to the private customer network 170. Communication overthe network segments 125, 135 may be effected by exchanging discreteframes or packets of data according to pre-defined protocols, such asthe Transmission Control Protocol/Internet Protocol (TCP/IP) and theOpenID Connect (OIDC) protocol, although other protocols, such as theUser Datagram Protocol (UDP) or the HyperText Transfer Protocol Secure(HTTPS) may also be advantageously employed.

Illustratively, the customer network 170 may be configured as a HyperConverged Infrastructure (HCI) cluster of nodes 200 deployed in a datacenter 175. The nodes 200 may be configured to provide various services,such compute, storage and management services, for information, i.e.,data and metadata, organized and stored on storage devices of thecluster. To that end, the nodes 200 include storage and compute nodesorganized as the HCI cluster to provide a distributed storagearchitecture configured to service storage requests issued by one ormore clients of the cluster. The compute nodes include hardwareresources, such processors, memory and networking, to provide computeservices in a deployment of the cluster, while the storage nodes includesuch hardware resources, along with storage, to provide data storage andmanagement services in the cluster. The nodes 200 may be interconnectedby one or more cluster network switches 180 and include functionalcomponents that cooperate to provide a distributed, scale-out storagearchitecture of the cluster. The components of each node 200 includehardware and software functionality that enable the node to connect toand service one or more clients over the public computer network 140, aswell as to a storage array 190 of storage devices, to thereby render theservices in accordance with the distributed storage architecture.

The embodiments described herein are directed to an augmented reality(AR) diagnostic tool 150 embodied as a software application on aportable device that employs AR infrastructure to enable a user tolocate a failed node of the HCI cluster in the customer data center 175and, with minimal interaction, diagnose causes and providerecommendations to functionally and quickly repair the failed node(i.e., get back online). The portable device may be a computer embodiedas wearable visualization technology (e.g., a hand-held smart phone, ARglasses, AR contact lenses, or include an AR visualization device as aperipheral) and configured to execute the software application, e.g., anAR diagnostic (ARD) application 160, that is installable from the cloudprovider 110 or other software delivery (e.g., compact disc). Onceinstalled, the ARD application 160 transforms the portable device intothe ARD tool 150 that is ready for use by the user, e.g., a customerservice technician, an administrator or technician of the data center,in the data center 175 to locate and repair one or more failed clusternodes. In response to a failure, malfunction or suboptimal operation thecluster node sends diagnostic and configuration information (i.e.,failure/malfunction information), including a node identifier (ID), ofthe failed node to the cloud-based analytics service 122 of the cloudprovider 110. As used herein, failure and/or malfunction informationincludes diagnostic and configuration information, including a node ID,of a node that may have failed or that may be operating outside of oneor more specified norms (e.g., deemed malfunctioning or operatingsub-optimally according to one or more reference parameters), but maystill be functioning and capable of communication. The failureinformation also notifies the technician of the HCI cluster node failureusing, e.g., the node ID of the failed node. The technician may thenactivate the ARD tool 150 and AR infrastructure to locate and repair thefailed node.

FIG. 2 is a block diagram of a node 200 illustratively embodied as acomputer system having one or more processing units (processors) 210, amain memory 220, a non-volatile random access memory (NVRAM) 230, one ormore network interfaces 240, one or more storage controllers 250, one ormore cluster interfaces 260, and a USB interface 280 coupled to a beacontransmitter module 285, all of which are interconnected by a system bus290. In other embodiments, the beacon transmitter may be a stand-alonedevice within a chassis of the node (e.g., a battery-powered emitter)and not communicably coupled to the node. The network interface 240 mayinclude one or more ports adapted to couple the node 200 to theclient(s) over computer network 140, which may include point-to-pointlinks, wide area networks, virtual private networks implemented over thepublic network (Internet) or a shared local area network. The networkinterface 240 thus includes the mechanical, electrical and signalingcircuitry needed to connect the node to the network 140, which mayembody an Ethernet network.

The main memory 220 may include memory locations that are addressable bythe processor 210 for storing software programs and data structuresassociated with the embodiments described herein. The processor 210 may,in turn, include processing elements and/or logic circuitry configuredto execute the software programs, such as compute service 222, datamanagement service 224, and storage service 226, and manipulate the datastructures. An operating system 225, portions of which are typicallyresident in memory 220 and executed by the processing elements (e.g.,processor 210), functionally organizes the node by, inter alia, invokingoperations in support of the services implemented by the node. To thatend, the operating system 225 includes an application programminginterface (API) 228 for servicing requests, illustratively issued assystem calls, from applications, such as ARD application 160. A suitableoperating system 225 may include a general-purpose operating system,such as the UNIX® series, the Linux® operating system, the FreeBSD®operating system (and the like) or Microsoft Windows® series ofoperating systems, or an operating system with configurablefunctionality such as microkernels and embedded kernels. It will beapparent to those skilled in the art that other processing and memorymeans, including various computer readable media, may be used to storeand execute program instructions pertaining to the embodiments herein.

The storage controller 250 cooperates with the services implemented onthe node 200 to access information requested by the client. Theinformation is preferably stored on storage devices such as solid-statedrives (SSDs) 270, illustratively embodied as flash storage devices, ofstorage array 190. Note that any applicable storage media, such asmagnetic disk drives, may be used. In an embodiment, the flash storagedevices may be block-oriented devices (i.e., drives accessed as blocks)based on NAND flash components, e.g., single-level-cell (SLC) flash,multi-level-cell (MLC) flash or triple-level-cell (TLC) flash, althoughit will be understood to those skilled in the art that otherblock-oriented, non-volatile, solid-state electronic devices (e.g.,drives based on storage class memory components) may be advantageouslyused with the embodiments described herein. The storage controller 250may include one or more ports having I/O interface circuitry thatcouples to the SSDs 270 over an I/O interconnect arrangement, such as aconventional serial attached SCSI (SAS), serial ATA (SATA) topology, andPeripheral Component Interconnect (PCI) express.

Each cluster interface 260 may include one or more ports adapted tocouple the node 200 to the other node(s) of the data center 175. In anembodiment, a plurality of Ethernet ports (e.g., 10 Gbps) included inthe one or more interfaces may be used for internode (or client)communication, although it will be apparent to those skilled in the artthat other types of protocols and interconnects may be utilized withinthe embodiments described herein. The NVRAM 230 may include a back-upbattery or other built-in last-state retention capability (e.g.,non-volatile semiconductor memory such as storage class memory) that iscapable of maintaining data in light of a failure to the node andcluster environment.

FIG. 3 illustrates an example workflow 300 of the ARD tool 150configured to employ AR infrastructure to locate, diagnose and providerecommendations to repair a failed node 200 of the HCI cluster 175 inthe customer data center. Prior to shipment, each node 200 of the HCIcluster 175 is equipped with a beacon (e.g., iBeacon) utility 310 thatis installed and programmed with the node's ID 325. The beacon utility310 may interact with the beacon transmitter module 285 to operate as aslow, steady transmitter of a beacon signal 320, in response to afailure or malfunction of a node. In response to a failure, malfunctionor suboptimal operation of a node, the activated ARD tool 150 may detectthe transmitted beacon signal 320 and employ the node ID 325 to locatethe specific failed node 200 in accordance with a proximity analysisfeature of the AR infrastructure. In an embodiment, the beacon operatescontinuously whether the node is failed or not. Notably, as used hereina “failed node” includes nodes that are operating outside of one or morespecified norms (e.g., deemed malfunctioning or operating sub-optimallyaccording to one or more reference parameters), but may still befunctioning and capable of communication. Such proximity analysisenables location of the failed node in the data center 175 from atypical in-building distance (e.g., 200 m). Illustratively, each beacontransmitter 285 has a locator range of approximately 70 meters(standard) to 450 meters (long range).

Upon detection of the beacon signal indicating the HCI node failure, thetechnician activates the ARD application 160, authenticates into the HCIcluster 175, and inputs the failed node ID 325. Illustratively, the ARDtool 150 operates as a receiver/locater for a specific beacon utility310 with a specific node ID 325. The application 160 guides (e.g.,visually, auditorily, or haptically) the technician in the direction ofthe transmitted beacon signal 320. In an embodiment, the ARD tool 150further invokes the proximity analysis feature to direct the technicianto the specific location of the failed node 200 in the data center 175by emitting an increasingly repeated, higher audible ‘peep’ sound 330 asthe technician arrives proximally closer to the node 200. In anembodiment, the proximity analysis feature is based on determining arelative detected signal strength of the beacon signal. For example,when arriving within one-foot proximity, the higher frequency audible‘peep’ sound 330 rapidly repeats indicating the technician has locatedthe failed node 200. In other embodiments, increasingly repeated cues,such as visual indicators and/or haptic pulses, are used to communicategreater proximity to the failed node.

The ARD tool 150 may then invoke an image detection feature of the ARinfrastructure to detect one or more suspect components of the node 200that may be the cause of the failure. In an embodiment, image detectionmay be implemented by rendering a digital image (e.g., jpeg) of theouter perimeter of the node, such as the front panel 340 and back panel345 of the node. The ARD application 160 may be “trained,” e.g., usingconventional machine learning techniques, to identify components visiblein and interfaces accessible from the panels of the node. For example,the application 160 may be trained to identify one or more network ports350 protruding through the back panel 345 of the node 200 and configuredto connect the node to one or more clients over the computer network 140or to the cluster (network) switch 180 of the cluster 175.

The image detection feature may also allow the ARD application 160 ofthe ARD tool 150 to “lock,” i.e., identify the type of component andspecific instance of the component within the node (e.g., a 1 Gbpsethernet port, identified as device “/dev/igb0” by the operating systemof the node) onto the component of the failed node using the node ID 325and thereafter issue system calls, such as API calls 360 (e.g., usingthe identifier for the component), to the API 228 of the operatingsystem 225 of the node to request information, such as configuration,performance and historical parameters, about the component. For example,the ARD application 160 may lock onto the network port 350 and issue anAPI call 360 to the operating system 225 requesting informationpertaining to data “velocity rate” (e.g., input/output data transfer persecond) parameters of the network port 350. The requested informationmay then be transmitted to the ARD application 160 and displayed asdiagnostic information on the wearable visualization technology (e.g.,AR screen, AR lens) of the ARD tool 150 in accordance with avisualization feature of the AR infrastructure. Notably, an imagerecognition database is maintained for each type of component for thenode back panels and correlated with a back panel layout for each typeof node as well as component identification for the operating system ofthe node.

FIG. 4 is an example rendering of information displayed as a visualoverlay on the wearable visualization technology of the ARD tool.Illustratively, the data velocity rates of the network port 350requested by the ARD application 160 may be rendered as a visual overlaydisposed over an image of the back panel 345 of the node 200 anddisplayed as diagnostic information on the wearable visualizationtechnology 370 of the ARD tool within a field of view 400 of thetechnician. Notably the image of the back panel may be rendereddigitally on a screen and/or seen through a field of view lens includedas part of the ARD tool. The visualization feature of the ARinfrastructure may render the information in various geometric shapesand dimensions, as well as colors as one or more false color mappingsdepicting relative magnitudes or features of the information beingdisplayed. For example, the data velocity rates may be rendered as3-dimensional (3D) bar charts 410 indicating how fast data is flowingthrough the network port 350. The visual rendering may be furtherdepicted (e.g., color-coded, shaped coded) to represent parameters ofthe component (e.g., data velocities of network port 350) at variouspredetermined units for both past and present time intervals. In sum,the renderings may provide visualization of various characteristics ofthe cluster node as visual overlays disposed over an actual storage orcompute node of the cluster that present basic diagnostic (and, in someembodiments, heartbeat) information by telemetry of the failed node on adisplay of the ARD tool via a display, e.g., a wearable AR screen, suchas an AR headset.

In an embodiment, the ARD application 160 may interact with severaltelemetry sources (e.g., the failed node, administrative nodes of thecluster and, illustratively, the cloud-based diagnostic service 124) toreceive requested telemetry data and provide diagnostic information. Forinstance, the API 228 of the operating system 225 may provide updates ofmost recent activity for selected predetermined time intervals (e.g., 5seconds). Cloud telemetry data such as, e.g., predictive analytics, maybe provided by the cloud-based analytics service 122 on differentpredetermined time interval (e.g., 5-minute to multi-month) updates.These telemetry sources may be accessed from the ARD application 160on-demand via standard communication protocols, such as HTTPS.

The requested telemetry data from the sources may be rendered on thewearable visualization technology 370 (e.g., AR screen) and presentedas, e.g., histogram overlays in the field of view of the technician suchthat the technician simultaneously sees the actual hardware nodeoverlaid with the analytics information on the screen. For example, thehistograms may render data transfer rate history, latency history, etc.In an embodiment, the ARD application 160 may provide a zoom capabilityfrom historical to real-time data, so that when the technician zooms inon a histogram to display the most real-time rendering, data is pulledfrom the operating system API 228. When the technician zooms out to aday/month granularity, the data may be pulled from the analytics service122, e.g., short-term analytics servers, and when fully zoomed out,pulled from long-term analytics servers of the service 122. Notably, theanalytics service 122 is configured to receive data regarding theparameters of components of the nodes of the cluster and organize (e.g.,archive and migrate) the data temporally among one or more analyticsservers, e.g., short-term analytics servers for data gather during acurrent calendar week and long-term analytics servers for data greaterthan 2 calendar weeks old.

The wearable visualization technology 370 may be sensitive to variouslocations on both the front panel 340 and back panel 345 of the failednode 200. Typically, the back panel 345 of the node contains severalnetwork (connection) ports 350. In an embodiment, a front panel viewdiagnostic result may recommend proceeding to the back panel 345 of thefailed node for deeper diagnostics, so that the technician may positionto the back panel, point the display of the ARD tool 150 to focus on thenetwork ports 350, and lock on to the ports for deeper analysis.Information and diagnostics pertaining to the network ports 350, such asdata and port management information, may be rendered on the wearablevisualization technology 370 (e.g., AR screen) by moving depicted crosshairs over the ports in the technician's field of view to lock on to aspecific port. In this manner a gesture by the technician using thefield of view may select a component on a node back panel (e.g., movinga head-mounted AR display using depicted cross hairs over thecomponent). For example, if the displayed port information includesanalysis of a detected latency problem, the technician may be referredto repair recommendations to resolve the issue. That is, the ARD toolmay reflexively display information (i.e., identifier, parameters, etc.)regarding components as they are targeted in the field of view, i.e., atype of AR “mouse-over.”

In an embodiment, the AR infrastructure may enhance problem detectionand resolution of the failed node by enabling the technician to forward(i.e., stream) information relating to a diagnostic analysis of thefailure from the ARD application 160 to vendor-specific supportengineering to facilitate a resolution of the problem, e.g., byconfirming an order of specific parts and/or providing on-handrecommendations and/or alternatives to repair the node (i.e., quicklyget the node back online). Additionally, the diagnostic analysis as wellas the overall problem detection and resolution experience may berecorded by the ARD application 160 and uploaded, e.g., via a HTTPSconnection 375, to a predictive analytics database 380 of thecloud-based analytics service 122 (FIG. 3 ) for inclusion in archival ofHCI cluster historical artifacts for future reference as needed. The ARDtool 150 thus enables an in-field technician to perform initial analysisand self-diagnosis of storage and/or compute node failure in a HCIcluster, capture information relating to the analysis and diagnosis, andstream the captured information to a predictive analytics service 122for future reference in a historical database 380 related to the HCIcluster 175. From the perspective of the database, the stream representsintegration of an AR analysis input source from the ARD tool 150 withthe predictive analytics service. As a result, reliable operation of thecluster is increased, down time is reduced, and suboptimal operationrectified, such as increasing network throughput by alleviatingbottlenecks otherwise unforeseen, but for information integrated fromthe database.

FIG. 5 illustrates an exemplary simplified procedure for utilizing theARD tool (and ARD application) to locate, diagnose and providerecommendations to repair a failed node of the HCI cluster. Theprocedure 500 starts at block 505 and proceeds to block 510 where theARD application of the ARD tool receives a transmitted beacon signal,including the node ID, of the failed node in the cluster. At block 515,the ARD application guides the ARD tool (i.e., a user of the tool) inthe direction of the transmitted beacon signal to locate the failed nodein the cluster in accordance with the proximity analysis feature of theAR infrastructure (e.g., determining a relative signal strength of thebeacon signal). At block 520, the ARD application invokes the imagedetection feature of the AR infrastructure to detect and lock onto oneor more components (i.e., identify the component within the node, suchas an operating system device identification) of the node suspected tobe the cause of the failure. At block 525, the ARD application issuesone or more system calls to the operating system of the node to requestinformation about the component. At block 530, the requested informationis displayed on the visualization technology of the ARD tool as a, e.g.,wearable visual overlay disposed over an image of the node. At block535, the displayed information is analyzed, e.g., by the ARD applicationoptionally with information from the analytics service 122, to provide adiagnosis of the suspected component and/or node failure and, at block540, the diagnosis is forwarded from the ARD application tovendor-specific support engineering to facilitate resolution and repairof the component and/or node. At block 545, the ARD application uploadsthe diagnosis to a predictive analytics database of the cloud-basedanalytics service for future reference, and the procedure ends at block550.

While there have been shown and described illustrative embodiments forproviding an ARD tool that employs AR infrastructure to enable atechnician to locate and repair a failed node of an HCI cluster in adata center, it is to be understood that various other adaptations andmodifications may be made within the spirit and scope of the embodimentsherein. For example, embodiments have been shown and described hereinwith relation to training of the ARD application to identify components,such as network ports, embedded in the panels of the node to enableanalysis of data velocity parameters of the ports. However, theembodiments in their broader sense are not so limited, and may, in fact,allow for identification of other node components and analysis of otherparameters associated with those components.

For instance, the ARD application 160 may be trained to identify storagedevices (such as SSDs 270) within the storage array 190 connected to thenode 200. The image detection feature of the AR infrastructure may alsoallow the ARD application of the ARD tool 150 to lock onto the SSDs 270and issue API calls 360 to the operating system 225 of the node torequest information pertaining to failure rates of the SSDs, e.g., howmany SSDs of a specific type failed over a predetermined time intervaland at what rate. The requested information may then be rendered as afalse-color map, i.e., a “heat map,” visual overlay disposed over thestorage array 190 and displayed on the wearable visualization technology370 of the ARD tool 150. The heat map may be further embodied as a“live” map configured to display failure (and correction) rates of theSSDs 270 in real-time.

In addition, the ARD application 160 may be trained to identify circuitboards (not shown) within the node 200 and, in particular, busesconnecting storage devices. The image detection feature may allow theARD application and tool to lock onto the boards and issue API calls 360to the operating system 225 to request information pertaining to, e.g.,signal congestion on the buses. The congestion may be visualized viaoverlays on the wearable visualization technology 370 using 3D models ofthe circuit boards to enable isolation of the circuit board in the nodeand subsequent debugging at the circuit board level. The ARD application160 may be configured to instruct the technician (e.g., step-by-step)during node disassembly and circuit board debug.

Advantageously, the ARD application 160 and associated AR infrastructuredescribed herein provide an ARD tool 150 that enables a customer servicefield technician to quickly locate and self-diagnose a failed node in anHCI cluster 175 within the customer's data center. The ARD tool 150provides AR visualization rendering of problem discovery and guidance toinstruct the technician through repair of the node failure toeffectively reduce the number of vendor engineering support engagements.In essence, the ARD tool 150 enables in-field technicians to performin-house support failure repairs, thereby augmenting the technicalcapability of the technician through AR visualization infrastructure.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware encoded on a tangible (non-transitory) computer-readable medium(e.g., disks, electronic memory, and/or CDs) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly, this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true spirit and scope of theembodiments herein.

What is claimed is:
 1. A method comprising: in response to a triggeringcondition associated with a computer system residing within a datacenter and representing a node of a plurality of types of nodes, guidinga technician in a direction of the node by detecting by a portabledevice being used by the technician a beacon signal emitted by the node,wherein the beacon signal includes a node identifier (ID) of the node;in response to a determination the portable device is within apredetermined proximity to the node, identifying a suspect component ofthe node visible within or accessible from a panel of an outer perimeterof the node by performing image detection on the panel of the node andwith reference to an image recognition database that includes imagesrepresenting each of a plurality of types of components associated withvarious panels of the plurality of types of nodes; and displaying onaugmented reality (AR) visualization technology of the portable devicediagnostic information based on telemetry associated with the node byrendering the diagnostic information as a visual overlay superimposedover a view of the panel of the node within a field of view of thetechnician.
 2. The method of claim 1, wherein the triggering conditionis indicative of a failure, a malfunction or suboptimal operation of thenode.
 3. The method of claim 1, wherein the node is one of a pluralityof storage and compute nodes organized as a hyperconvergedinfrastructure (HCI) cluster to provide a distribute storagearchitecture to service storage requests issued by one or more clientsof the HCI cluster.
 4. The method of claim 1, wherein the node includesa beacon utility that was installed and programmed with the node IDprior to deployment within the data center.
 5. The method of claim 1,wherein the suspect component comprises a network port.
 6. The method ofclaim 1, wherein each component type of the plurality of types ofcomponents represented within the image recognition database iscorrelated with a component device identifier, and wherein the methodfurther includes issuing one or more system calls to an operating systemof the node to request the diagnostic information about the suspectcomponent based on the component device identifier of the suspectcomponent.
 7. The method of claim 1, wherein said rendering thediagnostic information as a visual overlay further comprises renderingthe diagnostic information as parameters of the suspect component in oneor more of geometric shapes, dimensions and colors.
 8. A non-transitorycomputer-readable storage medium embodying a set of instructions, whichwhen executed by a processing resource of a portable device causes theportable device to: guide a user of the portable device in a directionof a malfunctioning node of a plurality of types of nodes within a datacenter by detecting a beacon signal emitted by the malfunctioning node,wherein the beacon signal includes a node identifier (ID) associatedwith the malfunctioning node; in response to a determination theportable device is within a predetermined proximity of themalfunctioning node, identify a suspect component of the malfunctioningnode visible within or accessible from a panel of an outer perimeter ofthe malfunctioning node by performing image detection on the panel ofthe malfunctioning node and with reference to an image recognitiondatabase that includes images representing each of a plurality of typesof components associated with various panels of the plurality of typesof nodes; and display on visualization technology of the portable devicediagnostic information based on telemetry associated with themalfunctioning node by rendering the diagnostic information as anaugmented reality (AR) visual overlay superimposed over a real-worldview of the panel of the malfunctioning node within a field of view ofthe user.
 9. The non-transitory computer-readable storage medium ofclaim 8, wherein each component type of the plurality of types ofcomponents represented within the image recognition database iscorrelated with a component device identifier, and wherein the methodfurther includes issuing one or more system calls to an operating systemof the node to request the diagnostic information about the suspectcomponent based on the component device identifier of the suspectcomponent.
 10. The non-transitory computer-readable storage medium ofclaim 8, wherein said rendering the diagnostic information as a visualoverlay further comprises rendering the diagnostic information asparameters of the suspect component in one or more of geometric shapes,dimensions and colors.
 11. The non-transitory computer-readable storagemedium of claim 10, wherein the parameters are rendered asmulti-dimensional bar charts and color-coded to represent predeterminedunits for past and present time intervals.
 12. The non-transitorycomputer-readable storage medium of claim 10, wherein the suspectcomponent comprises a network port, and wherein the parameters representa data transfer rate associated with the network port or a latencyassociated with the network port.
 13. The non-transitorycomputer-readable storage medium of claim 10, wherein the portabledevice comprises a smart phone, augmented reality (AR) glasses, awearable AR screen, or an AR headset.
 14. A portable device comprising:a processing resource; a non-transitory computer-readable medium,coupled to the processing resource, having stored therein instructionsthat when executed by the processing resource cause the portable deviceto: guide a user of the portable device in a direction of a node of aplurality of types of nodes within a data center by detecting a beaconsignal emitted by the node, wherein the beacon signal includes a nodeidentifier (ID) of the node; in response to a determination the portabledevice is within a predetermined proximity to the node, identifying asuspect component of the node visible within or accessible from a panelof an outer perimeter of the node by performing image detection on thepanel of the node and with reference to an image recognition databasethat includes images representing each of a plurality of types ofcomponents associated with various panels of the plurality of types ofnodes; and displaying on visualization technology of the portable devicediagnostic information based on telemetry associated with the node byrendering the diagnostic information as a visual overlay superimposedover a view of the panel of the node within a field of view of the user.15. The portable device of claim 14, wherein the portable devicecomprises a smart phone, augmented reality (AR) glasses, a wearable ARscreen, or an AR headset.
 16. The portable device of claim 14, whereineach component type of the plurality of types of components representedwithin the image recognition database is correlated with a componentdevice identifier, and wherein execution of the instructions by theprocessing resource further cause the portable device to issue one ormore system calls to an operating system of the node to request thediagnostic information about the suspect component based on thecomponent device identifier of the suspect component.
 17. The portabledevice of claim 14, wherein said rendering the diagnostic information asa visual overlay further comprises rendering the diagnostic informationas parameters of the suspect component in one or more of geometricshapes, dimensions and colors.
 18. The portable device of claim 17,wherein the suspect component comprises a network port, and wherein theparameters represent a data transfer rate associated with the networkport or a latency associated with the network port.
 19. The portabledevice of claim 17, wherein the parameters are rendered as histogramsoverlaid in the field of view.
 20. The portable device of claim 19,wherein the histograms render one of data transfer rate history andlatency history.